How to Make Your Company a Data Company

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Your business data has value, and you can use that data to drive growth and revenues in four easy steps. We call this becoming a data company.

What is a Data Company?

First, what is a “data company”?

A data-driven organization is any business that gathers and applies data to reach their business goals. They have made the collection and use of their data one of their core competencies. What’s important to note here is that a data company can be in any industry – from auto dealerships to manufacturing and retail. Of course, what initially comes to mind are the tech behemoths of our time like Facebook, Google, and Amazon. But non-tech companies are also getting in on the action en masse.

Take, for example, traditional retailers Walmart and Target’s recent launches of online shopping platforms and customer personalization. This is the use of data to drive operational efficiency, new market growth, and a better understanding of their customer base.

Regardless of your industry or focus, achieving these outcomes begins and ends with your data. With the rise of increasingly affordable business intelligence (BI) tools, even small or midsized local businesses are becoming disruptive data companies. In fact, the IDC predicts that big data and business analytics will be a $274 billion global industry by 2022, underscoring a universal investment in data as the next frontier.

In the current business climate, it is imperative that your organization leverages your data to understand your business and make better, data-driven decisions. The ability to turn insight into action allows you to be both growth-focused and respond proactively to changing market conditions. Something we are all too familiar with in “The Year of Disruption” 2020.

Virtually any business can become a data company by following these four simple, universal steps.

Step 1: Have a Data Strategy

Every great data company has a data strategy – a clear vision of the role data plays in your business and what you need to do to make sure your data is always fully leveraged. To create your own data strategy, start by listing all of your data sources. Inventory data, customer reviews, website traffic, email lists, and financial reports are just a few examples.

Next, identify opportunities in your data to make your business more profitable. A great way to do this is to think of questions that, if answered, could help you either increase revenue or reduce costs. For instance, you may want to know which of your products and services have the highest ROI, or which prospecting activities are most likely to lead to a sale. Good questions always tie back to your bottom line and need to be answered on a regular basis.

Having outside, experienced guidance during this step ensures that you have a solid foundation for future success. A data consultant like our team at Eide Bailly can help your organization define your data goals and assess your data sources to effectively develop a winning data strategy.

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Step 2: Centralize Your Data

The second step is the one that most businesses skip, and consequently, it’s also the number one reason why most organizations that set out to become data companies fail.

You may have noticed in Step 1 that many of your highest-value questions require data from multiple sources. This can be a serious challenge, especially if two of your data sources aren’t inherently compatible. If you’re not careful, you could end up bogged down manually collecting and combining your data in Excel spreadsheets. Doing things this way is tedious, error-prone, and unsustainable.

Spreadsheets on their own can work for smaller businesses that aren’t growing. For anyone else, spreadsheets eventually need to be supplemented with more robust reporting tools.

The best way to collect and centralize your data is with a data warehouse.

The Data Warehouse Difference

A data warehouse isn’t just for storage. It’s a special database where your data is stitched together to make “one version of the truth” for your organization. Data warehouses also let you define all your business logic in a single location. This keeps your data error-free and helps you avoid getting conflicting numbers.

Many businesses try to skip Step 2 and go straight to Step 3: visualizing their data with reporting tools. But 99% of the time, this is a huge mistake. While “viz tools” are great for building dashboards, graphs, and reports, they tend to overpromise on their ability to connect to your data sources. At the end of the day, data visualizations simply work best when they’re used in conjunction with a data warehouse.

Building and maintaining a data warehouse is no easy task. It requires buying expensive hardware and software and hiring a team of skilled BI developers. If these challenges sound daunting, consider leveraging Eide Bailly’s proven data warehouse approach instead. We leverage leading cloud data platforms like Snowflake and customize the data warehouse to fit your organization’s unique needs. This takes the headache and internal workload off your team and into the experienced hands of a team that has more than a decade of experience in the data warehouse space.

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Step 3: Visualize Your Data With Reporting and Analytics

With your data properly centralized and modeled in your data warehouse, you’re ready to visualize it. Data visualization is the process of turning your data into meaningful charts, graphs, and other visual representations that can be used to help you make smarter decisions.

Well-made dashboards and reports can be easily shared with anyone in your organization. There is no shortage of great viz tools for completing this step, but our personal favorite is Tableau.

Tableau is the leader in data visualization and analytics with tools that you can easily use, even if you’re not a data scientist. With the ability to customize the way you see your data, you’ll be able to quickly find the information you need to make data-driven decisions. Tableau is also easily scalable, meaning that it both seamlessly grows with your business and expands across departments based on your needs.

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Once you’ve got your viz tool in place, you’re ready to start answering the questions you asked in Step 1. As you get more comfortable with your new business intelligence system, keep identifying and answering additional questions.

Step 4: Advance With Advanced Analytics

Visualizing your data in reports is great, but it’s just the tip of the iceberg in terms of what your new data warehouse and visualization tool can help you accomplish. To really take advantage of your new BI setup, you need to start conducting advanced analytics.

Basic analytics are about reporting where you’re at or where you’ve been. For example, it answers questions like: How much inventory do we have? What were our total sales last month?

Advanced analytics, on the other hand, helps you see where you’re going. The questions become: How much will sales increase if we open a new location? What are our projected earnings based on past performance? What is the average lifetime value of our customers?

Applications of advanced analytics include what-if analysis for exploring different strategic paths and even more sophisticated capabilities, like machine learning. The advanced analytics process uses powerful mathematical techniques to interpret data. Patterns, groupings, and correlations in data sets are identified using statistical methods and machine-based techniques, such as deep learning. This allows you to make predictions about future behavior.

These complex predictive and prescriptive analyses often require a highly-skilled data scientist who knows computer coding languages, like Python and the R language. Again, you can hire your own internal data science team or leverage an experienced partner like Eide Bailly that can provide strategic data services and support. With the advent of big data, advanced analytics is more and more common. Either you embrace it and take advantage of your data’s full potential, or you risk getting left behind.

Next Steps

Once you have your data strategy, a data warehouse, a data visualization tool, and an advanced analytics approach, it’s time to roll out to your team and start embedding data analytics into your organization. Becoming a data company is truly a mindset. It is a way of looking at your organization through the lens of your business data and using data-driven insights to make decisions and lead.

Once your foundation is established, you can start using your data to quickly and efficiently pioneer new business intelligence focused initiatives in your organization. Ultimately, a data-driven company becomes a growing, competitive company.


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